The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided in this application is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
It has been recognized that simulations of biological processes defined by simple rules for individual actors within the simulation may yield complex emergent behavior. It has yet to be appreciated that actors within a simulation of biological processes may represent computer processing rules and algorithms, and a simulation of an evolutionary process containing those rules and algorithms may yield useful complex algorithms for providing useful predictions among sets of data.
Steven L. Peck, “Simulation as experiment: a philosophical reassessment for biological modeling,” for example, describes several uses of simulations in an ecological context: simulations may be used to model “digital” life, in which creatures are allowed to compete, reproduce, and evolve in a computer “environment”; simulations may also be appropriate to model processes that “are inescapably complex.” Similarly, a python implementation of agent-based modeling exists, project Mesa, which is licensed under the Apache License, Version 2.0 (available at http://www.apache.org/licenses/LICENSE-2.0). Neither Peck nor Project Mesa teaches or suggests that simulations can be used as a tool to simulate the process of model generation itself, in which digital agents in a simulation comprise algorithms capable of predicting outcomes based on data, and where agents can mate with similar digital agents to generate improved algorithms for predicting outcomes based on data.
Thus, there is still a need for improved model generation that can take advantage of advances in agent-based modeling environments.